{ "info": { "author": "Dr. Fayyaz Minhas, Amina Asif, Muhammad Arif", "author_email": "", "bugtrack_url": null, "classifiers": [ "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "\ufeffPython Implementation of CAF\u00c9-Map: Context Aware Feature Mapping for mining high dimensional biomedical data \nas described in paper [1] \n\nAuthors: \n\nDr. Fayyaz Minhas (afsar pieas dot edu dot pk)\nAmina Asif (a.asif.shah01 gmail dot com )\nMuhammad Arif (syedmarif2003 yahoo dot com)\nDownloaded From: http://faculty.pieas.edu.pk/fayyaz/software.html#cafemap or https://github.com/foxtrotmike/cafemap\n\nThis folder contains the package \"cafeMap\" and all example files. \n\nINSTALLATION INSTRUCTIONS FOR THE PACKAGE:\n1. Go to directory ..../cafemap-master/cafeMap in command prompt\n2. Execute the command: pip install .\nor\npython setup.py install\n\n\nCafeMap package has the following modules:\n\ncafemap.py: class implementation of cafemap according to the algorithm presented in the paper\n\ninstance.py: contains the definition of an instance to be used by cafemap\n\ncv.py: parallel implementation of cross validation methods is present in this file.\n\nllc.py: This module implements the approximate Locality Constrained Linear Coding as described in the 2010 paper \nby Wang et al. [2]. Given array of datapoints X (N x d) and codebook C (c x d), it returns a vector of approximated \npoints Y = G * C. LLC introduces sparsity by forcing those coefficients of a given data point that correspond to codebook \nvectors which are not that point's k-nearest neighbors. LLC also uses regularization. \n\nutils.py: contains utility functions to facilitate compilation of results\n\n\n\nFollowing files contain the code that generated the results published in the study:\n\nresults_table1.py produces the results presented in Table 1 of [1]\nl-shaped.py: produces the plots presented in Figure 2 of [1]\n2x2checker.py: produces the plots presented in Figure 3 of [1]\ntoy_circle.py: produces the plots for circular data as presented in Figure 4 of [1]. \narcene.py: produces the plots presented in Figure 6 of [1]\nprostate.py: produces the clustering analysis plots presented in Figure 8 of [1]\n\nNote: the plots may vary from the published according to the selection of parameters while running the code.\n\nExample.py illustrates the use of cafeMap package. To use parallel version of the training method, joblib module should be \ninstalled. All the parameters are explained in comments. \n\n\n\nReferences:\n[1]F. ul A. A. Minhas, A. Asif, and M. Arif, \u201cCAF\u00c9-Map: Context Aware Feature Mapping \nfor mining high dimensional biomedical data,\u201d Computers in Biology and Medicine, vol. 79, pp. 68\u201379, Dec. 2016.\n\n[2] Wang, Jinjun, Jianchao Yang, Kai Yu, Fengjun Lv, T. Huang, and Yihong Gong. \n\u201cLocality-Constrained Linear Coding for Image Classification.\u201d In 2010 IEEE Conference on Computer Vision and \nPattern Recognition (CVPR), 3360\u201367, 2010. doi:10.1109/CVPR.2010.5540018.\n\nAcknowledgments: We used the ROC module implemented by Dr. Asa Ben-Hur and Mike Hamilton which follows:\nTheoretical and pratical concepts from \nFawcett, T. ROC graphs: Notes and pratical considerations\nfor data mining researchers. 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